Overview

Dataset statistics

Number of variables23
Number of observations1874087
Missing cells3969791
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory432.5 MiB
Average record size in memory242.0 B

Variable types

DateTime1
Numeric21
Categorical1

Alerts

Unit_4_Power is highly correlated with Turbine_Guide Vane Opening and 2 other fieldsHigh correlation
Turbine_Guide Vane Opening is highly correlated with Unit_4_Power and 2 other fieldsHigh correlation
Turbine_Pressure Drafttube is highly correlated with Unit_4_Power and 2 other fieldsHigh correlation
Turbine_Pressure Spiral Casing is highly correlated with Turbine_Guide Vane OpeningHigh correlation
Bolt_1_Steel tmp is highly correlated with Bolt_4_Tensile and 4 other fieldsHigh correlation
Bolt_1_Tensile is highly correlated with Bolt_2_Tensile and 7 other fieldsHigh correlation
Bolt_2_Tensile is highly correlated with Bolt_1_Tensile and 7 other fieldsHigh correlation
Bolt_3_Tensile is highly correlated with Bolt_1_Tensile and 7 other fieldsHigh correlation
Bolt_4_Tensile is highly correlated with Bolt_1_Steel tmp and 4 other fieldsHigh correlation
Bolt_5_Tensile is highly correlated with Bolt_4_TensileHigh correlation
Bolt_6_Tensile is highly correlated with Bolt_1_Steel tmp and 9 other fieldsHigh correlation
Bolt_1_Torsion is highly correlated with Bolt_1_Tensile and 7 other fieldsHigh correlation
Bolt_2_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
Bolt_3_Torsion is highly correlated with Bolt_1_Steel tmp and 9 other fieldsHigh correlation
Bolt_4_Torsion is highly correlated with Bolt_1_Steel tmp and 9 other fieldsHigh correlation
Bolt_5_Torsion is highly correlated with Bolt_1_Tensile and 7 other fieldsHigh correlation
lower_bearing_vib_vrt is highly correlated with Turbine_Pressure Drafttube and 1 other fieldsHigh correlation
turbine_bearing_vib_vrt is highly correlated with Unit_4_Power and 3 other fieldsHigh correlation
Unit_4_Power is highly correlated with Unit_4_Reactive Power and 5 other fieldsHigh correlation
Unit_4_Reactive Power is highly correlated with Unit_4_Power and 3 other fieldsHigh correlation
Turbine_Guide Vane Opening is highly correlated with Unit_4_Power and 5 other fieldsHigh correlation
Turbine_Pressure Drafttube is highly correlated with Unit_4_Power and 4 other fieldsHigh correlation
Turbine_Pressure Spiral Casing is highly correlated with Unit_4_Power and 3 other fieldsHigh correlation
Turbine_Rotational Speed is highly correlated with Bolt_4_Tensile and 1 other fieldsHigh correlation
Bolt_1_Steel tmp is highly correlated with Bolt_1_Tensile and 8 other fieldsHigh correlation
Bolt_1_Tensile is highly correlated with Bolt_1_Steel tmp and 10 other fieldsHigh correlation
Bolt_2_Tensile is highly correlated with Bolt_1_Steel tmp and 10 other fieldsHigh correlation
Bolt_3_Tensile is highly correlated with Bolt_1_Steel tmp and 10 other fieldsHigh correlation
Bolt_4_Tensile is highly correlated with Unit_4_Power and 9 other fieldsHigh correlation
Bolt_5_Tensile is highly correlated with Unit_4_Power and 7 other fieldsHigh correlation
Bolt_6_Tensile is highly correlated with Bolt_1_Steel tmp and 10 other fieldsHigh correlation
Bolt_1_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
Bolt_2_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
Bolt_3_Torsion is highly correlated with Bolt_1_Steel tmp and 9 other fieldsHigh correlation
Bolt_4_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
Bolt_5_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
lower_bearing_vib_vrt is highly correlated with turbine_bearing_vib_vrtHigh correlation
turbine_bearing_vib_vrt is highly correlated with lower_bearing_vib_vrtHigh correlation
Unit_4_Power is highly correlated with Turbine_Guide Vane Opening and 1 other fieldsHigh correlation
Turbine_Guide Vane Opening is highly correlated with Unit_4_Power and 1 other fieldsHigh correlation
Turbine_Pressure Spiral Casing is highly correlated with Turbine_Guide Vane OpeningHigh correlation
Bolt_1_Steel tmp is highly correlated with Bolt_2_Torsion and 2 other fieldsHigh correlation
Bolt_1_Tensile is highly correlated with Bolt_2_Tensile and 6 other fieldsHigh correlation
Bolt_2_Tensile is highly correlated with Bolt_1_Tensile and 6 other fieldsHigh correlation
Bolt_3_Tensile is highly correlated with Bolt_1_Tensile and 6 other fieldsHigh correlation
Bolt_4_Tensile is highly correlated with Bolt_5_TensileHigh correlation
Bolt_5_Tensile is highly correlated with Bolt_4_TensileHigh correlation
Bolt_6_Tensile is highly correlated with Bolt_1_Tensile and 6 other fieldsHigh correlation
Bolt_1_Torsion is highly correlated with Bolt_1_Tensile and 6 other fieldsHigh correlation
Bolt_2_Torsion is highly correlated with Bolt_1_Steel tmp and 8 other fieldsHigh correlation
Bolt_3_Torsion is highly correlated with Bolt_1_Steel tmp and 7 other fieldsHigh correlation
Bolt_4_Torsion is highly correlated with Bolt_1_Steel tmp and 2 other fieldsHigh correlation
Bolt_5_Torsion is highly correlated with Bolt_1_Tensile and 5 other fieldsHigh correlation
turbine_bearing_vib_vrt is highly correlated with Unit_4_PowerHigh correlation
Unit_4_Power is highly correlated with Unit_4_Reactive Power and 11 other fieldsHigh correlation
Unit_4_Reactive Power is highly correlated with Unit_4_Power and 7 other fieldsHigh correlation
Turbine_Guide Vane Opening is highly correlated with Unit_4_Power and 12 other fieldsHigh correlation
Turbine_Pressure Drafttube is highly correlated with Unit_4_Power and 16 other fieldsHigh correlation
Turbine_Pressure Spiral Casing is highly correlated with Unit_4_Power and 5 other fieldsHigh correlation
Turbine_Rotational Speed is highly correlated with Turbine_Guide Vane Opening and 12 other fieldsHigh correlation
mode is highly correlated with Unit_4_Power and 12 other fieldsHigh correlation
Bolt_1_Steel tmp is highly correlated with Bolt_1_Tensile and 9 other fieldsHigh correlation
Bolt_1_Tensile is highly correlated with Unit_4_Power and 18 other fieldsHigh correlation
Bolt_2_Tensile is highly correlated with Unit_4_Power and 17 other fieldsHigh correlation
Bolt_3_Tensile is highly correlated with Unit_4_Power and 18 other fieldsHigh correlation
Bolt_4_Tensile is highly correlated with Unit_4_Power and 15 other fieldsHigh correlation
Bolt_5_Tensile is highly correlated with Unit_4_Power and 16 other fieldsHigh correlation
Bolt_6_Tensile is highly correlated with Unit_4_Power and 17 other fieldsHigh correlation
Bolt_1_Torsion is highly correlated with Unit_4_Reactive Power and 12 other fieldsHigh correlation
Bolt_2_Torsion is highly correlated with Bolt_1_Steel tmp and 9 other fieldsHigh correlation
Bolt_3_Torsion is highly correlated with Unit_4_Power and 17 other fieldsHigh correlation
Bolt_4_Torsion is highly correlated with Turbine_Pressure Drafttube and 14 other fieldsHigh correlation
Bolt_5_Torsion is highly correlated with Unit_4_Reactive Power and 13 other fieldsHigh correlation
Bolt_6_Torsion is highly correlated with Unit_4_Reactive Power and 14 other fieldsHigh correlation
lower_bearing_vib_vrt is highly correlated with Turbine_Pressure Drafttube and 9 other fieldsHigh correlation
turbine_bearing_vib_vrt is highly correlated with Turbine_Rotational Speed and 1 other fieldsHigh correlation
Unit_4_Power has 124087 (6.6%) missing values Missing
Unit_4_Reactive Power has 124087 (6.6%) missing values Missing
Turbine_Guide Vane Opening has 124087 (6.6%) missing values Missing
Turbine_Pressure Drafttube has 124087 (6.6%) missing values Missing
Turbine_Pressure Spiral Casing has 124087 (6.6%) missing values Missing
Turbine_Rotational Speed has 124087 (6.6%) missing values Missing
Bolt_1_Steel tmp has 124087 (6.6%) missing values Missing
Bolt_1_Tensile has 124087 (6.6%) missing values Missing
Bolt_2_Tensile has 124087 (6.6%) missing values Missing
Bolt_3_Tensile has 124087 (6.6%) missing values Missing
Bolt_4_Tensile has 124087 (6.6%) missing values Missing
Bolt_5_Tensile has 124087 (6.6%) missing values Missing
Bolt_6_Tensile has 124087 (6.6%) missing values Missing
Bolt_1_Torsion has 124087 (6.6%) missing values Missing
Bolt_2_Torsion has 124087 (6.6%) missing values Missing
Bolt_3_Torsion has 124087 (6.6%) missing values Missing
Bolt_4_Torsion has 124087 (6.6%) missing values Missing
Bolt_5_Torsion has 124087 (6.6%) missing values Missing
Bolt_6_Torsion has 124087 (6.6%) missing values Missing
lower_bearing_vib_vrt has 806069 (43.0%) missing values Missing
turbine_bearing_vib_vrt has 806069 (43.0%) missing values Missing
Turbine_Rotational Speed is highly skewed (γ1 = -33.27484819) Skewed
turbine_bearing_vib_vrt is highly skewed (γ1 = 75.51677077) Skewed
timepoints has unique values Unique
Unit_4_Reactive Power has 250525 (13.4%) zeros Zeros

Reproduction

Analysis started2022-03-07 09:17:40.480154
Analysis finished2022-03-07 09:24:34.774233
Duration6 minutes and 54.29 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

timepoints
Date

UNIQUE

Distinct1874087
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
Minimum1970-12-19 09:51:44
Maximum1971-01-25 11:06:48
2022-03-07T10:24:34.857366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:24:34.977111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Unit_4_Power
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1009985
Distinct (%)57.7%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean297.7542366
Minimum0
Maximum323.3023321
Zeros5220
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:35.127341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile202.4047217
Q1306.905898
median309.8456618
Q3311.2798767
95-th percentile311.9127031
Maximum323.3023321
Range323.3023321
Interquartile range (IQR)4.373978713

Descriptive statistics

Standard deviation33.87414197
Coefficient of variation (CV)0.1137654408
Kurtosis21.07544903
Mean297.7542366
Median Absolute Deviation (MAD)1.564250322
Skewness-3.925917999
Sum521069914.1
Variance1147.457494
MonotonicityNot monotonic
2022-03-07T10:24:35.256570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.317016633848
 
1.8%
311.335601825668
 
1.4%
311.279876724049
 
1.3%
311.428466817692
 
0.9%
311.409912116896
 
0.9%
311.35418715350
 
0.8%
311.465637213205
 
0.7%
311.224151611247
 
0.6%
311.261291510882
 
0.6%
311.205566410494
 
0.6%
Other values (1009975)1570669
83.8%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
05220
0.3%
100.04592711
 
< 0.1%
100.13035771
 
< 0.1%
100.22597831
 
< 0.1%
100.3292081
 
< 0.1%
100.39289151
 
< 0.1%
100.53104941
 
< 0.1%
100.62360311
 
< 0.1%
100.81382561
 
< 0.1%
100.88444991
 
< 0.1%
ValueCountFrequency (%)
323.30233211
< 0.1%
323.16092481
< 0.1%
323.13584841
< 0.1%
322.99691421
< 0.1%
322.87186051
< 0.1%
322.83290361
< 0.1%
322.6688931
< 0.1%
322.60787251
< 0.1%
322.50488241
< 0.1%
322.34388451
< 0.1%

Unit_4_Reactive Power
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1497381
Distinct (%)85.6%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean5.989380215
Minimum-38.1166523
Maximum49.10231842
Zeros250525
Zeros (%)13.4%
Negative194550
Negative (%)10.4%
Memory size14.3 MiB
2022-03-07T10:24:35.405802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-38.1166523
5-th percentile-7.928708852
Q10
median6.300166645
Q311.17088322
95-th percentile17.31713411
Maximum49.10231842
Range87.21897071
Interquartile range (IQR)11.17088322

Descriptive statistics

Standard deviation8.155809611
Coefficient of variation (CV)1.361711783
Kurtosis1.804237267
Mean5.989380215
Median Absolute Deviation (MAD)5.447398354
Skewness-0.3736303482
Sum10481415.38
Variance66.51723042
MonotonicityNot monotonic
2022-03-07T10:24:35.517719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0250525
 
13.4%
11.1364612672
 
< 0.1%
12.9383573549
 
< 0.1%
-6.71531438846
 
< 0.1%
7.54195737844
 
< 0.1%
10.2540893635
 
< 0.1%
5.44284105334
 
< 0.1%
5.74934911734
 
< 0.1%
4.87626552634
 
< 0.1%
24.3255977629
 
< 0.1%
Other values (1497371)1499098
80.0%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
-38.11665231
< 0.1%
-38.065908611
< 0.1%
-37.999445861
< 0.1%
-37.932983111
< 0.1%
-37.866520361
< 0.1%
-37.742192911
< 0.1%
-37.526092861
< 0.1%
-37.437645671
< 0.1%
-37.120165981
< 0.1%
-37.117994461
< 0.1%
ValueCountFrequency (%)
49.102318421
< 0.1%
48.878585371
< 0.1%
48.805384441
< 0.1%
48.489965151
< 0.1%
48.470226651
< 0.1%
48.135068861
< 0.1%
48.101344931
< 0.1%
47.799911071
< 0.1%
47.712724711
< 0.1%
47.438912151
< 0.1%

Turbine_Guide Vane Opening
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1402127
Distinct (%)80.1%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean91.05909656
Minimum0
Maximum96.18052034
Zeros38
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:35.645357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67.38302571
Q193.31226244
median94.04115003
Q394.484375
95-th percentile94.84641552
Maximum96.18052034
Range96.18052034
Interquartile range (IQR)1.172112556

Descriptive statistics

Standard deviation8.714152685
Coefficient of variation (CV)0.09569777226
Kurtosis28.33090308
Mean91.05909656
Median Absolute Deviation (MAD)0.4949990462
Skewness-4.37707176
Sum159353419
Variance75.93645701
MonotonicityNot monotonic
2022-03-07T10:24:35.768009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.4937515328931
 
1.5%
94.0062484713717
 
0.7%
94.7312545813052
 
0.7%
94.2437515310176
 
0.5%
94.256248479103
 
0.5%
93.768753058988
 
0.5%
94.478126538620
 
0.5%
94.428123477952
 
0.4%
94.021873477804
 
0.4%
94.496879587794
 
0.4%
Other values (1402117)1633863
87.2%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
038
< 0.1%
0.0086006234391
 
< 0.1%
0.90966233511
 
< 0.1%
0.91596468521
 
< 0.1%
0.92226703521
 
< 0.1%
0.92856938531
 
< 0.1%
0.93487173531
 
< 0.1%
0.94117408541
 
< 0.1%
0.94747643551
 
< 0.1%
0.95061717241
 
< 0.1%
ValueCountFrequency (%)
96.180520341
< 0.1%
96.179945761
< 0.1%
96.179878191
< 0.1%
96.178982491
< 0.1%
96.178318031
< 0.1%
96.178159461
< 0.1%
96.176373161
< 0.1%
96.176104781
< 0.1%
96.17600641
< 0.1%
96.175621041
< 0.1%

Turbine_Pressure Drafttube
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean157.7164612
Minimum135.3587107
Maximum273.4938392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:35.900455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum135.3587107
5-th percentile145.7232779
Q1148.8042407
median151.6418337
Q3156.6875506
95-th percentile218.8935253
Maximum273.4938392
Range138.1351284
Interquartile range (IQR)7.883309872

Descriptive statistics

Standard deviation19.09087877
Coefficient of variation (CV)0.1210455689
Kurtosis7.797569575
Mean157.7164612
Median Absolute Deviation (MAD)3.43053616
Skewness2.914216348
Sum276003807.1
Variance364.4616522
MonotonicityNot monotonic
2022-03-07T10:24:36.022446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.9552161
 
< 0.1%
148.50861861
 
< 0.1%
148.77754851
 
< 0.1%
148.87705781
 
< 0.1%
148.97656721
 
< 0.1%
149.07607661
 
< 0.1%
149.08270861
 
< 0.1%
148.94372621
 
< 0.1%
148.80299431
 
< 0.1%
148.66226241
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
135.35871071
< 0.1%
135.62494351
< 0.1%
135.71914451
< 0.1%
135.89870521
< 0.1%
136.17246691
< 0.1%
136.44622861
< 0.1%
136.47928511
< 0.1%
136.67286581
< 0.1%
136.73198511
< 0.1%
136.82562051
< 0.1%
ValueCountFrequency (%)
273.49383921
< 0.1%
272.69909921
< 0.1%
270.07902871
< 0.1%
270.05696081
< 0.1%
270.00710451
< 0.1%
269.9457441
< 0.1%
269.44624341
< 0.1%
269.44200981
< 0.1%
269.18956641
< 0.1%
269.0891611
< 0.1%

Turbine_Pressure Spiral Casing
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1749996
Distinct (%)> 99.9%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean5312.68101
Minimum3621.802561
Maximum5512.771511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:36.165066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3621.802561
5-th percentile5285.030474
Q15298.511469
median5303.461626
Q35316.882461
95-th percentile5388.68032
Maximum5512.771511
Range1890.96895
Interquartile range (IQR)18.37099173

Descriptive statistics

Standard deviation29.90342779
Coefficient of variation (CV)0.005628688743
Kurtosis49.58220875
Mean5312.68101
Median Absolute Deviation (MAD)6.594533398
Skewness0.2229312971
Sum9297191767
Variance894.2149938
MonotonicityNot monotonic
2022-03-07T10:24:36.281472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5303.3975252
 
< 0.1%
5303.3874092
 
< 0.1%
5303.3772922
 
< 0.1%
5303.3671762
 
< 0.1%
5310.7991811
 
< 0.1%
5309.9566791
 
< 0.1%
5309.9045851
 
< 0.1%
5310.0836691
 
< 0.1%
5310.2646211
 
< 0.1%
5310.4455731
 
< 0.1%
Other values (1749986)1749986
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
3621.8025611
< 0.1%
3959.3670321
< 0.1%
3966.4687321
< 0.1%
4021.3645651
< 0.1%
4115.014991
< 0.1%
4187.6514971
< 0.1%
4228.492471
< 0.1%
4308.2569161
< 0.1%
4384.663451
< 0.1%
4388.2033791
< 0.1%
ValueCountFrequency (%)
5512.7715111
< 0.1%
5512.5643551
< 0.1%
5512.3254571
< 0.1%
5512.3141571
< 0.1%
5512.086561
< 0.1%
5511.8476631
< 0.1%
5511.5912621
< 0.1%
5511.2115051
< 0.1%
5510.8683681
< 0.1%
5510.1454731
< 0.1%

Turbine_Rotational Speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1622104
Distinct (%)92.7%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean107.9520735
Minimum0.6264305743
Maximum108.3687515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:36.419498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.6264305743
5-th percentile107.9135464
Q1108.0112508
median108.0552881
Q3108.0889136
95-th percentile108.1635349
Maximum108.3687515
Range107.742321
Interquartile range (IQR)0.07766285212

Descriptive statistics

Standard deviation2.902934824
Coefficient of variation (CV)0.02689095938
Kurtosis1142.864152
Mean107.9520735
Median Absolute Deviation (MAD)0.03783035989
Skewness-33.27484819
Sum188916128.7
Variance8.42703059
MonotonicityNot monotonic
2022-03-07T10:24:36.537762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108.056251548552
 
2.6%
108.037498537531
 
2.0%
108.068748520441
 
1.1%
108.04375465399
 
0.3%
107.90000153599
 
0.2%
1083023
 
0.2%
108.07500462412
 
0.1%
108.21250151199
 
0.1%
107.80000311199
 
0.1%
107.81874851199
 
0.1%
Other values (1622094)1625446
86.7%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
0.62643057431
< 0.1%
0.64857921951
< 0.1%
0.67072786461
< 0.1%
0.69287650981
< 0.1%
0.71502515491
< 0.1%
0.73717380011
< 0.1%
0.75932244521
< 0.1%
0.78147109041
< 0.1%
0.80361973551
< 0.1%
0.82576838071
< 0.1%
ValueCountFrequency (%)
108.3687515599
< 0.1%
108.36874421
 
< 0.1%
108.36772511
 
< 0.1%
108.36557921
 
< 0.1%
108.36343291
 
< 0.1%
108.36128651
 
< 0.1%
108.35914011
 
< 0.1%
108.35699381
 
< 0.1%
108.35484741
 
< 0.1%
108.35290291
 
< 0.1%

mode
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size117.9 MiB
operation
1868659 
start
 
5428

Length

Max length9
Median length9
Mean length8.988414625
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoperation
2nd rowoperation
3rd rowoperation
4th rowoperation
5th rowoperation

Common Values

ValueCountFrequency (%)
operation1868659
99.7%
start5428
 
0.3%

Length

2022-03-07T10:24:36.650363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-07T10:24:36.717427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
operation1868659
99.7%
start5428
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Bolt_1_Steel tmp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean3.185046972
Minimum2.402152403
Maximum4.611985458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:36.972070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.402152403
5-th percentile2.458961191
Q12.805073628
median3.176575844
Q33.505449649
95-th percentile4.005146714
Maximum4.611985458
Range2.209833055
Interquartile range (IQR)0.700376021

Descriptive statistics

Standard deviation0.4765674752
Coefficient of variation (CV)0.1496265139
Kurtosis-0.3905919406
Mean3.185046972
Median Absolute Deviation (MAD)0.3406229853
Skewness0.2768017746
Sum5573832.202
Variance0.2271165584
MonotonicityNot monotonic
2022-03-07T10:24:37.087964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1339962651
 
< 0.1%
3.4049881991
 
< 0.1%
3.4111308061
 
< 0.1%
3.409834431
 
< 0.1%
3.4105528831
 
< 0.1%
3.4102048541
 
< 0.1%
3.4092544471
 
< 0.1%
3.4080710751
 
< 0.1%
3.4067814271
 
< 0.1%
3.4060631161
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
2.4021524031
< 0.1%
2.4021947181
< 0.1%
2.4022276311
< 0.1%
2.402231791
< 0.1%
2.4022999841
< 0.1%
2.4023369321
< 0.1%
2.4024271351
< 0.1%
2.4025029691
< 0.1%
2.4025288961
< 0.1%
2.4025501521
< 0.1%
ValueCountFrequency (%)
4.6119854581
< 0.1%
4.6117370841
< 0.1%
4.6114818481
< 0.1%
4.6111486231
< 0.1%
4.6110593871
< 0.1%
4.6109710231
< 0.1%
4.6108544441
< 0.1%
4.6107835921
< 0.1%
4.6105113551
< 0.1%
4.6103513661
< 0.1%

Bolt_1_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1613.279392
Minimum1522.556555
Maximum1640.509705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:37.214861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1522.556555
5-th percentile1601.316467
Q11604.473336
median1611.077385
Q31618.607052
95-th percentile1634.537949
Maximum1640.509705
Range117.9531501
Interquartile range (IQR)14.13371575

Descriptive statistics

Standard deviation10.63793685
Coefficient of variation (CV)0.00659398298
Kurtosis0.7401239958
Mean1613.279392
Median Absolute Deviation (MAD)7.173343379
Skewness0.508305651
Sum2823238937
Variance113.1657005
MonotonicityNot monotonic
2022-03-07T10:24:37.333806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1598.481391
 
< 0.1%
1618.2074071
 
< 0.1%
1618.2130051
 
< 0.1%
1618.2112141
 
< 0.1%
1618.2305141
 
< 0.1%
1618.2200181
 
< 0.1%
1618.2201791
 
< 0.1%
1618.2289241
 
< 0.1%
1618.2327891
 
< 0.1%
1618.2466391
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1522.5565551
< 0.1%
1523.6620821
< 0.1%
1524.0967431
< 0.1%
1524.5668941
< 0.1%
1525.2091621
< 0.1%
1525.4058711
< 0.1%
1525.7110271
< 0.1%
1526.089981
< 0.1%
1526.3316991
< 0.1%
1526.4324261
< 0.1%
ValueCountFrequency (%)
1640.5097051
< 0.1%
1640.5044171
< 0.1%
1640.4992631
< 0.1%
1640.4899951
< 0.1%
1640.489981
< 0.1%
1640.4881381
< 0.1%
1640.4869221
< 0.1%
1640.4710371
< 0.1%
1640.4691
< 0.1%
1640.4678691
< 0.1%

Bolt_2_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1490.365362
Minimum1425.511174
Maximum1505.45494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:37.476583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1425.511174
5-th percentile1482.892944
Q11484.862421
median1489.436049
Q31493.729896
95-th percentile1503.28517
Maximum1505.45494
Range79.94376588
Interquartile range (IQR)8.867474753

Descriptive statistics

Standard deviation6.480422689
Coefficient of variation (CV)0.00434821075
Kurtosis2.406608923
Mean1490.365362
Median Absolute Deviation (MAD)4.433842579
Skewness0.1449257944
Sum2608139384
Variance41.99587823
MonotonicityNot monotonic
2022-03-07T10:24:37.608894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1480.9899171
 
< 0.1%
1493.3522661
 
< 0.1%
1493.3550121
 
< 0.1%
1493.3585911
 
< 0.1%
1493.3458251
 
< 0.1%
1493.33681
 
< 0.1%
1493.3503031
 
< 0.1%
1493.3488121
 
< 0.1%
1493.3413761
 
< 0.1%
1493.3509251
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1425.5111741
< 0.1%
1425.6324221
< 0.1%
1426.1693921
< 0.1%
1426.4413911
< 0.1%
1426.5378431
< 0.1%
1426.8288251
< 0.1%
1427.0404341
< 0.1%
1427.0777091
< 0.1%
1427.2267851
< 0.1%
1427.3869151
< 0.1%
ValueCountFrequency (%)
1505.454941
< 0.1%
1505.454361
< 0.1%
1505.4503131
< 0.1%
1505.4497471
< 0.1%
1505.4496811
< 0.1%
1505.4496431
< 0.1%
1505.4399011
< 0.1%
1505.4397371
< 0.1%
1505.4368021
< 0.1%
1505.4353051
< 0.1%

Bolt_3_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1690.255756
Minimum1665.702272
Maximum1702.324087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:37.762862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1665.702272
5-th percentile1685.132914
Q11686.002291
median1689.644851
Q31692.937857
95-th percentile1699.1926
Maximum1702.324087
Range36.62181436
Interquartile range (IQR)6.935566244

Descriptive statistics

Standard deviation4.674528001
Coefficient of variation (CV)0.002765574372
Kurtosis-0.6365725843
Mean1690.255756
Median Absolute Deviation (MAD)3.542502031
Skewness0.5147094372
Sum2957947572
Variance21.85121203
MonotonicityNot monotonic
2022-03-07T10:24:37.881520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1684.2470791
 
< 0.1%
1693.1152411
 
< 0.1%
1693.1431051
 
< 0.1%
1693.1453081
 
< 0.1%
1693.1474841
 
< 0.1%
1693.1354481
 
< 0.1%
1693.1286391
 
< 0.1%
1693.1290891
 
< 0.1%
1693.1152181
 
< 0.1%
1693.1146581
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1665.7022721
< 0.1%
1665.8695441
< 0.1%
1665.9996971
< 0.1%
1666.1010291
< 0.1%
1666.1623041
< 0.1%
1666.231051
< 0.1%
1666.2805291
< 0.1%
1666.3155491
< 0.1%
1666.340031
< 0.1%
1666.3425131
< 0.1%
ValueCountFrequency (%)
1702.3240871
< 0.1%
1702.3233691
< 0.1%
1702.3102441
< 0.1%
1702.3068131
< 0.1%
1702.3054841
< 0.1%
1702.3051521
< 0.1%
1702.300221
< 0.1%
1702.2989591
< 0.1%
1702.2979171
< 0.1%
1702.2936921
< 0.1%

Bolt_4_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1602.896568
Minimum1549.426775
Maximum1608.264342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:38.019642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1549.426775
5-th percentile1599.373564
Q11602.372687
median1603.005521
Q31603.347662
95-th percentile1606.369163
Maximum1608.264342
Range58.83756659
Interquartile range (IQR)0.9749749708

Descriptive statistics

Standard deviation2.264478498
Coefficient of variation (CV)0.001412741497
Kurtosis103.6899413
Mean1602.896568
Median Absolute Deviation (MAD)0.4939146549
Skewness-6.14928541
Sum2805068995
Variance5.127862869
MonotonicityNot monotonic
2022-03-07T10:24:38.130375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1601.3599441
 
< 0.1%
1603.413261
 
< 0.1%
1603.4159971
 
< 0.1%
1603.4259451
 
< 0.1%
1603.4293461
 
< 0.1%
1603.4252581
 
< 0.1%
1603.4364151
 
< 0.1%
1603.4296741
 
< 0.1%
1603.4229161
 
< 0.1%
1603.4254781
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1549.4267751
< 0.1%
1552.7946421
< 0.1%
1553.6276621
< 0.1%
1553.6817791
< 0.1%
1553.9287831
< 0.1%
1553.9437321
< 0.1%
1554.1234581
< 0.1%
1554.4083311
< 0.1%
1554.490421
< 0.1%
1554.6184051
< 0.1%
ValueCountFrequency (%)
1608.2643421
< 0.1%
1608.2585781
< 0.1%
1608.2562551
< 0.1%
1608.2542931
< 0.1%
1608.2509291
< 0.1%
1608.2483721
< 0.1%
1608.2436411
< 0.1%
1608.2402861
< 0.1%
1608.2382611
< 0.1%
1608.2382311
< 0.1%

Bolt_5_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1637.421284
Minimum1587.783506
Maximum1642.895737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:38.277727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1587.783506
5-th percentile1634.457965
Q11636.91551
median1637.457668
Q31637.768623
95-th percentile1640.769686
Maximum1642.895737
Range55.11223126
Interquartile range (IQR)0.8531125678

Descriptive statistics

Standard deviation2.007034258
Coefficient of variation (CV)0.001225728698
Kurtosis102.1219391
Mean1637.421284
Median Absolute Deviation (MAD)0.3727212189
Skewness-5.814423566
Sum2865487246
Variance4.028186513
MonotonicityNot monotonic
2022-03-07T10:24:38.404550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1635.58571
 
< 0.1%
1637.1332351
 
< 0.1%
1637.1341811
 
< 0.1%
1637.1449211
 
< 0.1%
1637.1622561
 
< 0.1%
1637.1406851
 
< 0.1%
1637.148821
 
< 0.1%
1637.1417541
 
< 0.1%
1637.1203051
 
< 0.1%
1637.129331
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1587.7835061
< 0.1%
1592.8462891
< 0.1%
1593.4794031
< 0.1%
1594.0488771
< 0.1%
1594.1503971
< 0.1%
1594.7059021
< 0.1%
1594.9448631
< 0.1%
1595.0270541
< 0.1%
1595.0791771
< 0.1%
1595.1812511
< 0.1%
ValueCountFrequency (%)
1642.8957371
< 0.1%
1642.8931431
< 0.1%
1642.8915411
< 0.1%
1642.8847711
< 0.1%
1642.8837131
< 0.1%
1642.88251
< 0.1%
1642.8811591
< 0.1%
1642.8806291
< 0.1%
1642.8802081
< 0.1%
1642.8774211
< 0.1%

Bolt_6_Tensile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1749999
Distinct (%)> 99.9%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1679.709739
Minimum1608.898629
Maximum1694.804148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:38.554246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1608.898629
5-th percentile1675.197674
Q11676.753754
median1677.957932
Q31681.525809
95-th percentile1688.746165
Maximum1694.804148
Range85.90551874
Interquartile range (IQR)4.772054051

Descriptive statistics

Standard deviation4.540255719
Coefficient of variation (CV)0.002703000175
Kurtosis23.74065015
Mean1679.709739
Median Absolute Deviation (MAD)2.652642392
Skewness-1.17517669
Sum2939492044
Variance20.61392199
MonotonicityNot monotonic
2022-03-07T10:24:38.680362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1676.6654512
 
< 0.1%
1674.8488031
 
< 0.1%
1681.7491651
 
< 0.1%
1681.7718181
 
< 0.1%
1681.7726021
 
< 0.1%
1681.7714121
 
< 0.1%
1681.7704231
 
< 0.1%
1681.7828831
 
< 0.1%
1681.7909461
 
< 0.1%
1681.7793191
 
< 0.1%
Other values (1749989)1749989
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
1608.8986291
< 0.1%
1609.48551
< 0.1%
1609.9618041
< 0.1%
1610.0423621
< 0.1%
1610.3613621
< 0.1%
1610.3710121
< 0.1%
1610.5044351
< 0.1%
1610.857551
< 0.1%
1610.8718751
< 0.1%
1610.8941111
< 0.1%
ValueCountFrequency (%)
1694.8041481
< 0.1%
1694.8038151
< 0.1%
1694.7983711
< 0.1%
1694.7872691
< 0.1%
1694.7738631
< 0.1%
1694.7703641
< 0.1%
1694.7703251
< 0.1%
1694.7641451
< 0.1%
1694.7601491
< 0.1%
1694.7561761
< 0.1%

Bolt_1_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean178.4818931
Minimum175.3435812
Maximum183.4104736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:38.812934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum175.3435812
5-th percentile175.9793868
Q1176.8118688
median178.3134175
Q3179.6520081
95-th percentile182.5392164
Maximum183.4104736
Range8.066892401
Interquartile range (IQR)2.840139327

Descriptive statistics

Standard deviation2.019695826
Coefficient of variation (CV)0.01131597043
Kurtosis-0.4007311479
Mean178.4818931
Median Absolute Deviation (MAD)1.381476585
Skewness0.7010917208
Sum312343313
Variance4.079171228
MonotonicityNot monotonic
2022-03-07T10:24:38.929341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.75846041
 
< 0.1%
179.16243751
 
< 0.1%
179.15693791
 
< 0.1%
179.16114231
 
< 0.1%
179.16610321
 
< 0.1%
179.16076341
 
< 0.1%
179.16022971
 
< 0.1%
179.15815911
 
< 0.1%
179.16186591
 
< 0.1%
179.15866471
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
175.34358121
< 0.1%
175.3785321
< 0.1%
175.40560471
< 0.1%
175.43166411
< 0.1%
175.44487731
< 0.1%
175.46085331
< 0.1%
175.46918631
< 0.1%
175.47073721
< 0.1%
175.47127221
< 0.1%
175.47384911
< 0.1%
ValueCountFrequency (%)
183.41047361
< 0.1%
183.4103231
< 0.1%
183.40940011
< 0.1%
183.4091561
< 0.1%
183.40838431
< 0.1%
183.40798771
< 0.1%
183.40734321
< 0.1%
183.40715371
< 0.1%
183.40688241
< 0.1%
183.4067441
< 0.1%

Bolt_2_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean165.7813407
Minimum162.9430179
Maximum178.8814433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:39.074752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum162.9430179
5-th percentile163.038384
Q1163.2179514
median164.2139471
Q3165.5471214
95-th percentile175.8824486
Maximum178.8814433
Range15.93842535
Interquartile range (IQR)2.329169987

Descriptive statistics

Standard deviation4.113352209
Coefficient of variation (CV)0.02481191303
Kurtosis1.781098919
Mean165.7813407
Median Absolute Deviation (MAD)1.010166571
Skewness1.793270198
Sum290117346.3
Variance16.9196664
MonotonicityNot monotonic
2022-03-07T10:24:39.201279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163.9566131
 
< 0.1%
165.19052551
 
< 0.1%
165.18929361
 
< 0.1%
165.1837591
 
< 0.1%
165.18547511
 
< 0.1%
165.18948931
 
< 0.1%
165.188121
 
< 0.1%
165.18686021
 
< 0.1%
165.19049151
 
< 0.1%
165.19175971
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
162.94301791
< 0.1%
162.94324631
< 0.1%
162.94370161
< 0.1%
162.94498451
< 0.1%
162.94505961
< 0.1%
162.94511451
< 0.1%
162.94589931
< 0.1%
162.94651481
< 0.1%
162.94681981
< 0.1%
162.9468551
< 0.1%
ValueCountFrequency (%)
178.88144331
< 0.1%
178.74733421
< 0.1%
178.7253191
< 0.1%
178.71430671
< 0.1%
178.69722821
< 0.1%
178.67812981
< 0.1%
178.67109591
< 0.1%
178.66720591
< 0.1%
178.65957771
< 0.1%
178.65372771
< 0.1%

Bolt_3_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean146.2637473
Minimum145.715224
Maximum147.1180314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:39.346438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum145.715224
5-th percentile145.8786761
Q1146.0956022
median146.3354643
Q3146.456336
95-th percentile146.5029057
Maximum147.1180314
Range1.402807389
Interquartile range (IQR)0.3607337796

Descriptive statistics

Standard deviation0.2145015408
Coefficient of variation (CV)0.001466539349
Kurtosis-0.8349961838
Mean146.2637473
Median Absolute Deviation (MAD)0.15279629
Skewness-0.5701053591
Sum255961557.8
Variance0.04601091099
MonotonicityNot monotonic
2022-03-07T10:24:39.460194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146.28874111
 
< 0.1%
146.1889431
 
< 0.1%
146.19183681
 
< 0.1%
146.19658111
 
< 0.1%
146.19173781
 
< 0.1%
146.19495631
 
< 0.1%
146.19381281
 
< 0.1%
146.19205891
 
< 0.1%
146.19162721
 
< 0.1%
146.18672481
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
145.7152241
< 0.1%
145.71549381
< 0.1%
145.71597281
< 0.1%
145.71602091
< 0.1%
145.71602191
< 0.1%
145.71628121
< 0.1%
145.71666521
< 0.1%
145.71672421
< 0.1%
145.7168121
< 0.1%
145.71693551
< 0.1%
ValueCountFrequency (%)
147.11803141
< 0.1%
147.1177741
< 0.1%
147.11641181
< 0.1%
147.11538231
< 0.1%
147.11499781
< 0.1%
147.11372651
< 0.1%
147.1136831
< 0.1%
147.11294841
< 0.1%
147.11097461
< 0.1%
147.11086981
< 0.1%

Bolt_4_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean225.7179442
Minimum224.3098767
Maximum226.0518808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:39.753801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum224.3098767
5-th percentile225.3388864
Q1225.6090225
median225.7665844
Q3225.8673615
95-th percentile225.9807611
Maximum226.0518808
Range1.742004035
Interquartile range (IQR)0.2583390325

Descriptive statistics

Standard deviation0.200180336
Coefficient of variation (CV)0.0008868605315
Kurtosis-0.2610374907
Mean225.7179442
Median Absolute Deviation (MAD)0.1262145365
Skewness-0.7190875928
Sum395006402.4
Variance0.04007216692
MonotonicityNot monotonic
2022-03-07T10:24:39.871430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.53516971
 
< 0.1%
225.74764481
 
< 0.1%
225.74273071
 
< 0.1%
225.74542861
 
< 0.1%
225.7467731
 
< 0.1%
225.74624261
 
< 0.1%
225.74096731
 
< 0.1%
225.74256591
 
< 0.1%
225.74623531
 
< 0.1%
225.74631661
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
224.30987671
< 0.1%
224.37374341
< 0.1%
224.39789541
< 0.1%
224.41510811
< 0.1%
224.419331
< 0.1%
224.42300771
< 0.1%
224.43551381
< 0.1%
224.44102441
< 0.1%
224.44376471
< 0.1%
224.44377941
< 0.1%
ValueCountFrequency (%)
226.05188081
< 0.1%
226.05113821
< 0.1%
226.05074191
< 0.1%
226.04981931
< 0.1%
226.04854011
< 0.1%
226.0483171
< 0.1%
226.04808691
< 0.1%
226.04794451
< 0.1%
226.04776731
< 0.1%
226.04764141
< 0.1%

Bolt_5_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean298.6767971
Minimum297.7115398
Maximum301.2224312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:40.012834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum297.7115398
5-th percentile297.789422
Q1298.0816067
median298.5405256
Q3299.0896054
95-th percentile300.2391406
Maximum301.2224312
Range3.510891351
Interquartile range (IQR)1.007998715

Descriptive statistics

Standard deviation0.7708675946
Coefficient of variation (CV)0.00258094235
Kurtosis0.01106186409
Mean298.6767971
Median Absolute Deviation (MAD)0.4672881003
Skewness0.9380951222
Sum522684394.9
Variance0.5942368484
MonotonicityNot monotonic
2022-03-07T10:24:40.129867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297.78020851
 
< 0.1%
298.81437751
 
< 0.1%
298.82113081
 
< 0.1%
298.81683671
 
< 0.1%
298.81709421
 
< 0.1%
298.81799261
 
< 0.1%
298.81820371
 
< 0.1%
298.81728391
 
< 0.1%
298.82001361
 
< 0.1%
298.82521471
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
297.71153981
< 0.1%
297.71159371
< 0.1%
297.71161841
< 0.1%
297.71279841
< 0.1%
297.71295331
< 0.1%
297.71326161
< 0.1%
297.71328561
< 0.1%
297.71356451
< 0.1%
297.71373721
< 0.1%
297.71409121
< 0.1%
ValueCountFrequency (%)
301.22243121
< 0.1%
301.22030181
< 0.1%
301.21931161
< 0.1%
301.21840591
< 0.1%
301.21838031
< 0.1%
301.21821641
< 0.1%
301.21754691
< 0.1%
301.21721611
< 0.1%
301.21693311
< 0.1%
301.21688931
< 0.1%

Bolt_6_Torsion
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1750000
Distinct (%)100.0%
Missing124087
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean161.8113645
Minimum160.8970896
Maximum162.976624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:40.273118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum160.8970896
5-th percentile161.1481426
Q1161.6467493
median161.8120858
Q3162.0740421
95-th percentile162.3514002
Maximum162.976624
Range2.0795344
Interquartile range (IQR)0.4272928019

Descriptive statistics

Standard deviation0.3447037678
Coefficient of variation (CV)0.002130281571
Kurtosis-0.3116121044
Mean161.8113645
Median Absolute Deviation (MAD)0.2407468097
Skewness-0.4736301849
Sum283169887.9
Variance0.1188206875
MonotonicityNot monotonic
2022-03-07T10:24:40.389441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161.14809961
 
< 0.1%
162.05443961
 
< 0.1%
162.06172831
 
< 0.1%
162.05899621
 
< 0.1%
162.05854381
 
< 0.1%
162.0556921
 
< 0.1%
162.05776641
 
< 0.1%
162.05904971
 
< 0.1%
162.05888581
 
< 0.1%
162.05721061
 
< 0.1%
Other values (1749990)1749990
93.4%
(Missing)124087
 
6.6%
ValueCountFrequency (%)
160.89708961
< 0.1%
160.89761321
< 0.1%
160.89881381
< 0.1%
160.89941921
< 0.1%
160.89960971
< 0.1%
160.90131311
< 0.1%
160.9016751
< 0.1%
160.90225391
< 0.1%
160.90286231
< 0.1%
160.90288741
< 0.1%
ValueCountFrequency (%)
162.9766241
< 0.1%
162.96767291
< 0.1%
162.96089951
< 0.1%
162.95230191
< 0.1%
162.94421481
< 0.1%
162.93533031
< 0.1%
162.9215991
< 0.1%
162.9205371
< 0.1%
162.91764121
< 0.1%
162.91761991
< 0.1%

lower_bearing_vib_vrt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1068018
Distinct (%)100.0%
Missing806069
Missing (%)43.0%
Infinite0
Infinite (%)0.0%
Mean0.1454601354
Minimum0.05138795672
Maximum1.355756061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:40.513238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.05138795672
5-th percentile0.1109231054
Q10.1363742097
median0.146724563
Q30.155554487
95-th percentile0.1701840752
Maximum1.355756061
Range1.304368104
Interquartile range (IQR)0.01918027725

Descriptive statistics

Standard deviation0.02508660378
Coefficient of variation (CV)0.172463773
Kurtosis629.33574
Mean0.1454601354
Median Absolute Deviation (MAD)0.009476995633
Skewness17.04865374
Sum155354.0429
Variance0.0006293376892
MonotonicityNot monotonic
2022-03-07T10:24:40.637665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17249514161
 
< 0.1%
0.16826722921
 
< 0.1%
0.14902807461
 
< 0.1%
0.14398921581
 
< 0.1%
0.14817092581
 
< 0.1%
0.15022884251
 
< 0.1%
0.15418916071
 
< 0.1%
0.15190022531
 
< 0.1%
0.14073549461
 
< 0.1%
0.1561356291
 
< 0.1%
Other values (1068008)1068008
57.0%
(Missing)806069
43.0%
ValueCountFrequency (%)
0.051387956721
< 0.1%
0.051670997151
< 0.1%
0.053427747361
< 0.1%
0.054336584121
< 0.1%
0.056382742921
< 0.1%
0.057087390151
< 0.1%
0.057374695231
< 0.1%
0.057662921721
< 0.1%
0.057752402771
< 0.1%
0.058393689531
< 0.1%
ValueCountFrequency (%)
1.3557560611
< 0.1%
1.340082171
< 0.1%
1.3302386121
< 0.1%
1.3258035491
< 0.1%
1.3194400381
< 0.1%
1.3185871421
< 0.1%
1.3082084581
< 0.1%
1.2915313831
< 0.1%
1.2914939261
< 0.1%
1.2850684751
< 0.1%

turbine_bearing_vib_vrt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1068018
Distinct (%)100.0%
Missing806069
Missing (%)43.0%
Infinite0
Infinite (%)0.0%
Mean0.4567642388
Minimum0.130049381
Maximum53.16383118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-07T10:24:40.766509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.130049381
5-th percentile0.2507412018
Q10.4458939975
median0.4788168615
Q30.4995085754
95-th percentile0.5322704876
Maximum53.16383118
Range53.0337818
Interquartile range (IQR)0.05361457784

Descriptive statistics

Standard deviation0.242154741
Coefficient of variation (CV)0.5301525828
Kurtosis9478.955423
Mean0.4567642388
Median Absolute Deviation (MAD)0.023926605
Skewness75.51677077
Sum487832.4288
Variance0.05863891857
MonotonicityNot monotonic
2022-03-07T10:24:40.883601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.50088216541
 
< 0.1%
0.4892529711
 
< 0.1%
0.49788171751
 
< 0.1%
0.50251282051
 
< 0.1%
0.49473476081
 
< 0.1%
0.48715167991
 
< 0.1%
0.49970748021
 
< 0.1%
0.50337572851
 
< 0.1%
0.49811386241
 
< 0.1%
0.48860052991
 
< 0.1%
Other values (1068008)1068008
57.0%
(Missing)806069
43.0%
ValueCountFrequency (%)
0.1300493811
< 0.1%
0.13898670631
< 0.1%
0.14073252011
< 0.1%
0.14157810551
< 0.1%
0.14355763241
< 0.1%
0.14392344031
< 0.1%
0.14627420531
< 0.1%
0.14659642451
< 0.1%
0.15057825041
< 0.1%
0.15388923871
< 0.1%
ValueCountFrequency (%)
53.163831181
< 0.1%
43.792420171
< 0.1%
43.267692441
< 0.1%
42.206790011
< 0.1%
39.391287741
< 0.1%
37.42429871
< 0.1%
34.997168331
< 0.1%
31.720983851
< 0.1%
29.761367821
< 0.1%
28.321490541
< 0.1%

Interactions

2022-03-07T10:24:05.164362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:20:56.190984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:05.620050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:14.807615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:23.915284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:33.219904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:42.257984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:51.242847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:01.090465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:10.383371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:19.565073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:29.097569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:37.908609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:47.258512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:57.680447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:08.254440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:18.155729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:28.645561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:38.808397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:48.685383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:58.468068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:24:05.467901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:20:56.676224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:06.045928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:15.258687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-07T10:21:41.806130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:21:50.778704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:00.629619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:09.948509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:19.131227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:28.641710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:37.463458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:46.769239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:22:57.180874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:07.778710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:17.644675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:28.106610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:38.294423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:48.195111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:23:58.154271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-07T10:24:04.860327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-07T10:24:41.008374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-07T10:24:41.247937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-07T10:24:41.494872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-07T10:24:41.740342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-07T10:24:12.019219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-07T10:24:14.908106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-07T10:24:25.336707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-07T10:24:27.435283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

timepointsUnit_4_PowerUnit_4_Reactive PowerTurbine_Guide Vane OpeningTurbine_Pressure DrafttubeTurbine_Pressure Spiral CasingTurbine_Rotational SpeedmodeBolt_1_Steel tmpBolt_1_TensileBolt_2_TensileBolt_3_TensileBolt_4_TensileBolt_5_TensileBolt_6_TensileBolt_1_TorsionBolt_2_TorsionBolt_3_TorsionBolt_4_TorsionBolt_5_TorsionBolt_6_Torsionlower_bearing_vib_vrtturbine_bearing_vib_vrt
01970-12-19 09:51:44262.2043082.89903682.279976173.9552165310.799181107.964278operation4.1339961598.4813901480.9899171684.2470791601.3599441635.5857001674.848803175.758460163.956613146.288741225.535170297.780208161.148100NaNNaN
11970-12-19 09:51:45262.1043193.34463082.277248173.9898155311.219755107.964273operation4.1340781598.4774491480.9895281684.2616111601.3665081635.5884781674.823883175.755164163.951680146.284164225.527142297.771627161.145094NaNNaN
21970-12-19 09:51:46262.0043303.79022382.274520174.0244135311.640329107.964269operation4.1347311598.4793161481.0031881684.2705041601.3742541635.5834641674.841318175.764601163.952007146.283423225.522291297.777115161.144487NaNNaN
31970-12-19 09:51:47261.9043404.23581782.271792174.0590125312.060902107.964264operation4.1342701598.4901841481.0288271684.2706831601.3831791635.5813841674.843245175.763157163.953924146.283633225.535827297.772578161.144037NaNNaN
41970-12-19 09:51:48261.8043514.06475982.269064174.1538195312.405938107.964259operation4.1335831598.4940731481.0590171684.2710621601.3783911635.5917461674.872300175.760959163.951968146.286946225.534231297.774191161.151967NaNNaN
51970-12-19 09:51:49261.7043623.17051082.266336174.4220465312.533396107.964254operation4.1346181598.4989161481.0755211684.2766221601.3806011635.6078841674.924469175.757090163.952534146.292006225.531977297.777180161.148518NaNNaN
61970-12-19 09:51:50261.6043732.27513582.263608174.7011455312.647213107.964250operation4.1365561598.4940841481.0928011684.2910531601.3860811635.6154921674.965513175.757416163.950235146.286152225.528883297.775926161.141645NaNNaN
71970-12-19 09:51:51261.5043831.37976082.260880174.9802445312.761031107.964245operation4.1360611598.4967751481.1130921684.3065451601.3991831635.6178921674.987875175.765619163.950265146.283837225.529143297.777080161.142129NaNNaN
81970-12-19 09:51:52261.4043940.48438582.258152175.2593445312.874849107.964240operation4.1345271598.4955441481.1282321684.3123531601.4061991635.6250061675.019551175.766335163.947296146.282955225.528426297.778951161.147210NaNNaN
91970-12-19 09:51:53261.3044050.00075282.255425175.5123905312.980966107.964236operation4.1344871598.5122151481.1403181684.3258451601.4032331635.6476801675.048627175.761973163.953027146.284900225.532589297.770342161.150856NaNNaN

Last rows

timepointsUnit_4_PowerUnit_4_Reactive PowerTurbine_Guide Vane OpeningTurbine_Pressure DrafttubeTurbine_Pressure Spiral CasingTurbine_Rotational SpeedmodeBolt_1_Steel tmpBolt_1_TensileBolt_2_TensileBolt_3_TensileBolt_4_TensileBolt_5_TensileBolt_6_TensileBolt_1_TorsionBolt_2_TorsionBolt_3_TorsionBolt_4_TorsionBolt_5_TorsionBolt_6_Torsionlower_bearing_vib_vrtturbine_bearing_vib_vrt
18740771971-01-25 11:06:39308.5641815.99131994.408981158.1347215281.353945108.057530operation4.1950211637.4150511504.6271281701.6812641606.2910041640.7574721690.104363183.213624178.080908145.770720225.354055300.676153160.9489320.1447550.495444
18740781971-01-25 11:06:40308.5945505.66545194.412318158.0646925281.337848108.057524operation4.1950371637.3905481504.6068881701.6817301606.2643501640.7423641690.098270183.212339178.081184145.760906225.350832300.670715160.9484320.1389790.491025
18740791971-01-25 11:06:41308.6249195.23770994.415655157.9946625281.321750108.057517operation4.1939911637.3795891504.5846091701.6544351606.2715351640.7348831690.076644183.210993178.083366145.763441225.353740300.679040160.9510650.1451120.486698
18740801971-01-25 11:06:42308.6552874.80996794.418992157.9246335281.305653108.057511operation4.1930661637.3851601504.5588101701.6660221606.2772461640.7341501690.051294183.215709178.078190145.764820225.351832300.677870160.9494630.1389110.495108
18740811971-01-25 11:06:43308.6856564.38222594.422329157.8853265281.215929108.057505operation4.1939011637.3955161504.5656871701.6600311606.2715191640.7182691690.027884183.214837178.083603145.763042225.352981300.671435160.9549830.1368350.497243
18740821971-01-25 11:06:44308.7160253.97430994.425666157.9279055280.929965108.057498operation4.1939371637.3861151504.5578221701.6514201606.2765451640.7040711690.014705183.204777178.082932145.759475225.351989300.667011160.9498160.1593660.491265
18740831971-01-25 11:06:45308.7463934.10326294.429003157.9749255280.633358108.057492operation4.1932541637.3658651504.5460911701.6543011606.2718771640.7112501690.017029183.203293178.082287145.766584225.348279300.674243160.9478680.1559620.497242
18740841971-01-25 11:06:46308.7767624.47292994.432340158.0219455280.336751108.057486operation4.1932611637.3841331504.5386961701.6561431606.2500281640.6991421690.002008183.212397178.081678145.764007225.354785300.674078160.9476440.1411500.501525
18740851971-01-25 11:06:47308.8071314.84259794.435677158.0689665280.040144108.057479operation4.1927951637.3571411504.5315821701.6622011606.2456651640.6857821689.995135183.212669178.080734145.763103225.355483300.675584160.9440360.1609150.508167
18740861971-01-25 11:06:48308.8374995.21226494.439014158.1151375279.835631108.057473operation4.1926401637.3449851504.5250651701.6581311606.2862861640.6983921690.004759183.209714178.084189145.766948225.356568300.675411160.9475030.1630110.512094